Security
Defensive architectures to prevent data leakage, intercept adversarial payloads, and establish agent execution guardrails.
Observed Evidence
The Direct Experience
"While analyzing runtime vulnerabilities in autonomous systems, I watched an agent exfiltrate a customer database because a user submitted a base64-encoded command. I kept seeing the same security flaw: developers relying on English system prompts to govern agent behavior. The mechanism is Prompt Injection—probabilistic models treat instructions and data as a single execution context, allowing data to overwrite rules. This generalized into a core principle: prompt-level guardrails are fundamentally non-deterministic and easily bypassed. The broader implication is that AI agents cannot be secured via prompts; they require physical network-level interception and action-admissibility proxy gates."
Core Analytical Axioms
Forensically proven concepts in this operational boundary.
AI Agent Kill Switch
A deterministic runtime boundary that intercepts and terminates autonomous agent loops before they generate legal or financial liability.
AI agents are given tools (database access, API keys) without absolute boundaries, leading to recursive feedback loops that consume budget or delete data.
Prevents rogue agents from causing catastrophic operational crashes.
Adversarial Injection Shield
State-verification and schema-enforcement gates that isolate LLM prompt variables.
Attackers inject system-override prompts into input forms, bypassing guardrails and capturing database context.
Protects proprietary system instructions and blocks data exfiltration.
Shadow AI Scanner
Forensic evaluation to detect employee data exposure to unauthorized external models.
Employees copy-paste proprietary code, customer records, or financial spreadsheets into public LLM interfaces, breaching compliance.
Prevents intellectual property loss and guarantees compliance with SOC2 and GDPR.
Want to apply this to your organization?
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Richard Ewing — AI Economist & Capital Auditor